{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\BERNI\Forschung\GOAT-ARS\Submission\FPA\FPA replication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Dec 2025, 08:42:32

{com}. use "C:\BERNI\Forschung\GOAT projects\Harvard Dataverse\Gallup data.dta", clear

. su

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 10}ID {c |}{res}      1,691         846     488.294          1       1691
{txt}{space 8}Date {c |}{res}      1,691    23173.31    7.777371      23161      23186
{txt}{space 8}hurt {c |}{res}      1,691    .4967475    .5001373          0          1
{txt}{space 3}adjburden {c |}{res}      1,691    .5115316    .5000149          0          1
{txt}{space 5}socprog {c |}{res}      1,691    .4937907    .5001093          0          1
{txt}{hline 13}{c +}{hline 57}
{space 4}govblame {c |}{res}      1,691    .5038439    .5001331          0          1
{txt}industrybl~e {c |}{res}      1,691    .4967475    .5001373          0          1
{txt}{space 4}ngoblame {c |}{res}      1,691    .4866943    .4999708          0          1
{txt}{space 10}q1 {c |}{res}      1,691    2.162034    .8425105          1          3
{txt}{space 10}q2 {c |}{res}      1,691    1.771141    .8149606          1          3
{txt}{hline 13}{c +}{hline 57}
{space 10}q3 {c |}{res}      1,691    1.885275    1.061355          1          4
{txt}{space 10}q4 {c |}{res}      1,691    36.25133    43.31155          1         99
{txt}{space 9}q4a {c |}{res}        785    1.611465    .6313421          1          3
{txt}{space 10}q5 {c |}{res}      1,691    2.464222    .7640539          1          3
{txt}{space 10}q6 {c |}{res}      1,691    2.918391    3.229005          0         10
{txt}{hline 13}{c +}{hline 57}
{space 10}q7 {c |}{res}      1,691    6.550562    21.02687          1         99
{txt}{space 10}q8 {c |}{res}      1,691    6.159669    20.04827          1         99
{txt}{space 10}q9 {c |}{res}      1,691    1.183915    .5556607          1          3
{txt}{space 10}d1 {c |}{res}      1,691    1.124778     .330565          1          2
{txt}{space 10}d2 {c |}{res}      1,691    35.66351    12.87021         18         81
{txt}{hline 13}{c +}{hline 57}
{space 10}d3 {c |}{res}      1,691     3.53696    8.798818          1         99
{txt}{space 10}d4 {c |}{res}      1,691    3.517445     11.7475          1         99
{txt}{space 9}d4a {c |}{res}      1,165    1.909871    .2864891          1          2
{txt}{space 10}d5 {c |}{res}      1,691    1.388527    .4875597          1          2
{txt}{space 10}d7 {c |}{res}      1,691    5.517445    1.538891          1          8
{txt}{hline 13}{c +}{hline 57}
{space 10}d8 {c |}{res}      1,691    2.613247    1.959209          1          6
{txt}{space 6}Weight {c |}{res}      1,691           1    1.792804   .1593077   33.71968

{com}. des

{txt}Contains data from {res}C:\BERNI\Forschung\GOAT projects\Harvard Dataverse\Gallup data.dta
{txt}  obs:{res}         1,691                          
{txt} vars:{res}            27                          12 Nov 2025 11:32
{txt} size:{res}        60,876                          
{txt}{hline}
              storage   display    value
variable name   type    format     label      variable label
{hline}
{p 0 48}{res}{bind:ID             }{txt}{bind: int     }{bind:{txt}%12.0g    }{space 1}{bind:         }{bind:  }{res}{res}ID{p_end}
{p 0 48}{bind:Date           }{txt}{bind: int     }{bind:{txt}%tdD_m_Y  }{space 1}{bind:         }{bind:  }{res}{res}Interview Date{p_end}
{p 0 48}{bind:hurt           }{txt}{bind: byte    }{bind:{txt}%9.0g     }{space 1}{bind:         }{bind:  }{res}{res}T1. Some of these reforms may hurt some people in Pakistan{p_end}
{p 0 48}{bind:adjburden      }{txt}{bind: byte    }{bind:{txt}%9.0g     }{space 1}{bind:         }{bind:  }{res}{res}T2. The reform program includes measures to reduce the budget deficit, including{p_end}
{p 0 48}{bind:socprog        }{txt}{bind: byte    }{bind:{txt}%9.0g     }{space 1}{bind:         }{bind:  }{res}{res}T3. Pakistan’s social protection system, the Benazir Income Support Program (BIS{p_end}
{p 0 48}{bind:govblame       }{txt}{bind: byte    }{bind:{txt}%9.0g     }{space 1}{bind:         }{bind:  }{res}{res}T4. Members of the government criticized the IMF for insisting on cuts in electr{p_end}
{p 0 48}{bind:industryblame  }{txt}{bind: byte    }{bind:{txt}%9.0g     }{space 1}{bind:         }{bind:  }{res}{res}T5. The chairman of an important industry association criticized the IMF for ins{p_end}
{p 0 48}{bind:ngoblame       }{txt}{bind: byte    }{bind:{txt}%9.0g     }{space 1}{bind:         }{bind:  }{res}{res}T6. Civil society organizations criticized the IMF for insisting on cuts in elec{p_end}
{p 0 48}{bind:q1             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q1       }{bind:* }{res}{res}Q1. To what extent do you think the government of Pakistan under the leadership {p_end}
{p 0 48}{bind:q2             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q2       }{bind:  }{res}{res}Q2. To what extent do you think the IMF is responsible for this program?{p_end}
{p 0 48}{bind:q3             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q3       }{bind:  }{res}{res}Q3. To what extent do you approve or disapprove of this program?{p_end}
{p 0 48}{bind:q4             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q4       }{bind:  }{res}{res}Q4. Which party would you vote for if elections were held tomorrow?{p_end}
{p 0 48}{bind:q4a            }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q4a      }{bind:  }{res}{res}Opposition / Government{p_end}
{p 0 48}{bind:q5             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q5       }{bind:* }{res}{res}Q5. Would you like to record a statement of support for the governing coalition {p_end}
{p 0 48}{bind:q6             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q6       }{bind:* }{res}{res}Q6. On a scale of 0-10 where 0 means 'no confidence at all' and 10 means 'comple{p_end}
{p 0 48}{bind:q7             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q7       }{bind:* }{res}{res}Q7. Do you agree or disagree with the statement: I vote for the party that best {p_end}
{p 0 48}{bind:q8             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q8       }{bind:* }{res}{res}Q8. Do you agree or disagree with the statement: I vote for the party that most {p_end}
{p 0 48}{bind:q9             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:q9       }{bind:  }{res}{res}Q9. The International Monetary Fund is?{p_end}
{p 0 48}{bind:d1             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:d1       }{bind:  }{res}{res}D1. Gender{p_end}
{p 0 48}{bind:d2             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:         }{bind:  }{res}{res}D2. What is your age?{p_end}
{p 0 48}{bind:d3             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:d3       }{bind:  }{res}{res}D3. What is the highest level of education you have attained?{p_end}
{p 0 48}{bind:d4             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:d4       }{bind:  }{res}{res}D4. Employment type?{p_end}
{p 0 48}{bind:d4a            }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:d4a      }{bind:  }{res}{res}D4a. Do you work in the public or the private sector?{p_end}
{p 0 48}{bind:d5             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:d5       }{bind:  }{res}{res}D5. In what sort of area do you currently live?{p_end}
{p 0 48}{bind:d7             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:d7       }{bind:  }{res}{res}D7. What is your family�s/household�s total monthly income?{p_end}
{p 0 48}{bind:d8             }{txt}{bind: byte    }{bind:{txt}%12.0g    }{space 1}{bind:d8       }{bind:  }{res}{res}D8. What is your mother tongue?{p_end}
{p 0 48}{bind:Weight         }{txt}{bind: double  }{bind:{txt}%12.0g    }{space 1}{bind:         }{bind:  }{res}{res}Weight{p_end}
                                            {txt}* indicated variables have notes
{hline}
Sorted by: {res}ID

{com}. 
.   lab var hurt "hurt"

. 
.   lab var adjburden "adjburden"

. 
.   lab var socprog "socprog"

. 
.   
. 
.   
. 
. * Outcomes

. 
. 
. 
.   g prog_app=q3==3|q3==4

. 
.   lab var prog_app "Program support"

. 
.   g confimf=q6

. 
.   lab var confimf "Confidence in the IMF"

. 
. 
. 
.   
. 
. * Covariates

. 
. 
. 
.   g knowimf=q9==1

. 
.   lab var knowimf "Knowledgeable about the IMF"

. 
.   g male=d1==1

. 
.   lab var male "Male"

. 
.   g age=d2 

. 
.   lab var age "Age"

. 
.   g edulevel=d3 if d3<10
{txt}(14 missing values generated)

{com}. 
.   lab var edulevel "Education"

. 
.   g ftemp=d4==1

. 
.   lab var ftemp "Employed"

. 
.   g psj=d4a==1

. 
.   lab var psj "Public-sector"

. 
.   g urb=d5==1

. 
.   lab var urb "Urban"

. 
.   g inclevel=d7

. 
.   lab var inclevel "Income"

. 
.   g language=d8

. 
.   lab var language "Language"

. 
.  
. 
.   global X1 male age edulevel inclevel urb

. 
.   global X2 ftemp psj i.language 

. 
. 
. 
. 
. 
. * Main analysis 

. 
. ***************

. 
. 
. 
. * IMF design primes and support for IMF program

. 
. 
. 
.   ** Figure 1

. 
.   qui reg prog_app i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame [pw = Weight] if q9==1

. 
.   coefplot, keep(*hurt *adjburden *socprog) xline(0, lwidth(0.2) lpattern(dash)) scheme(s1mono) ytitle() xtitle() legend(off) level(90 90)
{res}
{com}. 
. 
. 
.   ** Figure A3

. 
.   margins i.hurt i.adjburden i.socprog, atmeans post 
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}     1,514
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:0.hurt}{space 10}{txt:=} {space 3}.5174473 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.hurt}{space 10}{txt:=} {space 3}.4825527 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.adjburden}{space 5}{txt:=} {space 3}.4627913 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.adjburden}{space 5}{txt:=} {space 3}.5372087 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.socprog}{space 7}{txt:=} {space 3}.5278491 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.socprog}{space 7}{txt:=} {space 3}.4721509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.govblame}{space 6}{txt:=} {space 4}.512311 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.govblame}{space 6}{txt:=} {space 4}.487689 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.industry~e}{space 4}{txt:=} {space 4}.529802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.industry~e}{space 4}{txt:=} {space 4}.470198 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.ngoblame}{space 6}{txt:=} {space 3}.5000006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.ngoblame}{space 6}{txt:=} {space 3}.4999994 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}hurt {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .3119667{col 26}{space 2} .0376399{col 37}{space 1}    8.29{col 46}{space 3}0.000{col 54}{space 4} .2381345{col 67}{space 3} .3857989
{txt}{space 10}1  {c |}{col 14}{res}{space 2}  .312913{col 26}{space 2} .0317904{col 37}{space 1}    9.84{col 46}{space 3}0.000{col 54}{space 4} .2505549{col 67}{space 3} .3752712
{txt}{space 12} {c |}
{space 3}adjburden {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .3549369{col 26}{space 2} .0394357{col 37}{space 1}    9.00{col 46}{space 3}0.000{col 54}{space 4} .2775823{col 67}{space 3} .4322915
{txt}{space 10}1  {c |}{col 14}{res}{space 2}  .275799{col 26}{space 2} .0295799{col 37}{space 1}    9.32{col 46}{space 3}0.000{col 54}{space 4} .2177768{col 67}{space 3} .3338212
{txt}{space 12} {c |}
{space 5}socprog {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .2826593{col 26}{space 2} .0307788{col 37}{space 1}    9.18{col 46}{space 3}0.000{col 54}{space 4} .2222856{col 67}{space 3} .3430331
{txt}{space 10}1  {c |}{col 14}{res}{space 2} .3456985{col 26}{space 2} .0401721{col 37}{space 1}    8.61{col 46}{space 3}0.000{col 54}{space 4} .2668994{col 67}{space 3} .4244976
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. 
.   coefplot, keep(*hurt *adjburden *socprog)  scheme(s1mono) ytitle() xtitle() legend(off) level(90 90)
{res}
{com}. 
. 
. 
.   ** Figure 2

. 
.   reg confimf i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame [pw = Weight] if q9==1
{txt}(sum of wgt is   1.4549e+03)

Linear regression                               Number of obs     = {res}     1,514
                                                {txt}F(6, 1507)        =  {res}     0.36
                                                {txt}Prob > F          = {res}    0.9033
                                                {txt}R-squared         = {res}    0.0074
                                                {txt}Root MSE          =    {res} 3.1919

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        confimf{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}1.hurt {c |}{col 17}{res}{space 2} .1728403{col 29}{space 2} .3312354{col 40}{space 1}    0.52{col 49}{space 3}0.602{col 57}{space 4} -.476891{col 70}{space 3} .8225716
{txt}{space 4}1.adjburden {c |}{col 17}{res}{space 2}-.2577566{col 29}{space 2} .3248364{col 40}{space 1}   -0.79{col 49}{space 3}0.428{col 57}{space 4} -.894936{col 70}{space 3} .3794227
{txt}{space 6}1.socprog {c |}{col 17}{res}{space 2} .2409119{col 29}{space 2} .3381914{col 40}{space 1}    0.71{col 49}{space 3}0.476{col 57}{space 4}-.4224638{col 70}{space 3} .9042876
{txt}{space 5}1.govblame {c |}{col 17}{res}{space 2} -.354744{col 29}{space 2} .3281368{col 40}{space 1}   -1.08{col 49}{space 3}0.280{col 57}{space 4}-.9983972{col 70}{space 3} .2889092
{txt}1.industryblame {c |}{col 17}{res}{space 2}   -.0899{col 29}{space 2} .3224793{col 40}{space 1}   -0.28{col 49}{space 3}0.780{col 57}{space 4}-.7224559{col 70}{space 3} .5426559
{txt}{space 5}1.ngoblame {c |}{col 17}{res}{space 2} .0581863{col 29}{space 2}  .336947{col 40}{space 1}    0.17{col 49}{space 3}0.863{col 57}{space 4}-.6027486{col 70}{space 3} .7191211
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 3.112065{col 29}{space 2} .4610042{col 40}{space 1}    6.75{col 49}{space 3}0.000{col 57}{space 4} 2.207787{col 70}{space 3} 4.016343
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. 
.   coefplot, keep(*hurt *adjburden *socprog) xline(0, lwidth(0.2) lpattern(dash)) scheme(s1mono) ytitle() xtitle() legend(off) level(90 90)
{res}
{com}. 
.  
. 
.   ** Figure A4

. 
.   margins i.hurt i.adjburden i.socprog, atmeans post 
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}     1,514
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:at}:{space 1}{res:{txt:0.hurt}{space 10}{txt:=} {space 3}.5174473 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.hurt}{space 10}{txt:=} {space 3}.4825527 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.adjburden}{space 5}{txt:=} {space 3}.4627913 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.adjburden}{space 5}{txt:=} {space 3}.5372087 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.socprog}{space 7}{txt:=} {space 3}.5278491 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.socprog}{space 7}{txt:=} {space 3}.4721509 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.govblame}{space 6}{txt:=} {space 4}.512311 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.govblame}{space 6}{txt:=} {space 4}.487689 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.industry~e}{space 4}{txt:=} {space 4}.529802 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.industry~e}{space 4}{txt:=} {space 4}.470198 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:0.ngoblame}{space 6}{txt:=} {space 3}.5000006 {txt:(mean)}}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:1.ngoblame}{space 6}{txt:=} {space 3}.4999994 {txt:(mean)}}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}hurt {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 2.901161{col 26}{space 2} .2351999{col 37}{space 1}   12.33{col 46}{space 3}0.000{col 54}{space 4} 2.439807{col 67}{space 3} 3.362515
{txt}{space 10}1  {c |}{col 14}{res}{space 2} 3.074001{col 26}{space 2} .2366405{col 37}{space 1}   12.99{col 46}{space 3}0.000{col 54}{space 4} 2.609821{col 67}{space 3}  3.53818
{txt}{space 12} {c |}
{space 3}adjburden {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 3.123034{col 26}{space 2} .2408382{col 37}{space 1}   12.97{col 46}{space 3}0.000{col 54}{space 4} 2.650621{col 67}{space 3} 3.595448
{txt}{space 10}1  {c |}{col 14}{res}{space 2} 2.865278{col 26}{space 2} .2268344{col 37}{space 1}   12.63{col 46}{space 3}0.000{col 54}{space 4} 2.420333{col 67}{space 3} 3.310222
{txt}{space 12} {c |}
{space 5}socprog {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 2.870818{col 26}{space 2} .2452442{col 37}{space 1}   11.71{col 46}{space 3}0.000{col 54}{space 4} 2.389762{col 67}{space 3} 3.351874
{txt}{space 10}1  {c |}{col 14}{res}{space 2}  3.11173{col 26}{space 2}  .230146{col 37}{space 1}   13.52{col 46}{space 3}0.000{col 54}{space 4}  2.66029{col 67}{space 3} 3.563171
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. 
.   coefplot, keep(*hurt *adjburden *socprog)  scheme(s1mono) ytitle() xtitle() legend(off) level(90 90)
{res}
{com}. 
. 
. 
. 
. 
. * Robustness test with demographics 

. 
. 
. 
.   qui reg prog_app i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame $X1 [pw = Weight] if q9==1

. 
.   est store c11

. 
.   qui reg prog_app i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame $X1 $X2 [pw = Weight]  if q9==1

. 
.   est store c12

. 
. 
. 
.   qui reg confimf i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame $X1 [pw = Weight] if q9==1

. 
.   est store c13

. 
.   qui reg confimf i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame $X1 $X2 [pw = Weight] if q9==1

. 
.   est store c14

. 
. 
. 
.   ** Table A2

. 
.   estout c1*, drop(_* *0.* *blame) starlevels(* .1 ** .05 *** .01) cells((b(star fmt(3)) se(par fmt(3)))) stats(N r2, fmt(0 3)) 
{res}
{txt}{hline 128}
{txt}                      c11                          c12                          c13                          c14                
{txt}                        b              se            b              se            b              se            b              se
{txt}{hline 128}
{txt}1.hurt      {res}        0.010         (0.047)        0.028         (0.040)        0.304         (0.315)        0.327         (0.308){txt}
{txt}1.adjburden {res}       -0.080*        (0.047)       -0.070*        (0.042)       -0.257         (0.306)       -0.279         (0.294){txt}
{txt}1.socprog   {res}        0.068         (0.049)        0.056         (0.043)        0.221         (0.312)        0.194         (0.303){txt}
{txt}male        {res}       -0.044         (0.054)       -0.034         (0.051)       -0.703**       (0.336)       -0.397         (0.341){txt}
{txt}age         {res}        0.002         (0.002)        0.001         (0.002)       -0.003         (0.011)       -0.005         (0.012){txt}
{txt}edulevel    {res}       -0.005         (0.021)        0.003         (0.019)        0.206         (0.127)        0.227*        (0.121){txt}
{txt}inclevel    {res}        0.009         (0.014)        0.016         (0.013)       -0.287**       (0.114)       -0.274**       (0.116){txt}
{txt}urb         {res}       -0.064         (0.043)       -0.075*        (0.040)       -0.137         (0.282)       -0.059         (0.286){txt}
{txt}ftemp       {res}                                    -0.044         (0.039)                                    -0.538**       (0.238){txt}
{txt}psj         {res}                                     0.025         (0.066)                                     0.283         (0.423){txt}
{txt}1.language  {res}                                     0.000             (.)                                     0.000             (.){txt}
{txt}2.language  {res}                                    -0.068         (0.062)                                     0.022         (0.454){txt}
{txt}3.language  {res}                                     0.315***      (0.090)                                     0.679         (0.663){txt}
{txt}4.language  {res}                                     0.212*        (0.109)                                    -1.552**       (0.626){txt}
{txt}5.language  {res}                                     0.030         (0.057)                                     0.118         (0.393){txt}
{txt}6.language  {res}                                     0.071         (0.055)                                    -0.342         (0.373){txt}
{txt}{hline 128}
{txt}N           {res}         1501                         1501                         1501                         1501                {txt}
{txt}r2          {res}        0.026                        0.080                        0.038                        0.053                {txt}
{txt}{hline 128}

{com}. 
. 
. 
.   
. 
. * Robustness test with logit 

. 
. 
. 
.   qui logit prog_app hurt adjburden socprog govblame industryblame ngoblame [pw = Weight] if q9==1

. 
.   est store c21

. 
.   qui logit prog_app hurt adjburden socprog govblame industryblame ngoblame $X1 [pw = Weight] if q9==1

. 
.   est store c22

. 
.   qui xi:logit prog_app hurt adjburden socprog govblame industryblame ngoblame $X1 ftemp psj i.language [pw = Weight] if q9==1

. 
.   est store c23

. 
.  
. 
.   ** Table A3 

. 
.   estout c2*, drop(_* *blame) starlevels(* .1 ** .05 *** .01) cells((b(star fmt(3)) se(par fmt(3)))) stats(N r2, fmt(0 3))  
{res}
{txt}{hline 99}
{txt}                      c21                          c22                          c23                
{txt}                        b              se            b              se            b              se
{txt}{hline 99}
{res}prog_app                                                                                           {txt}
{txt}hurt        {res}        0.005         (0.226)        0.048         (0.219)        0.145         (0.199){txt}
{txt}adjburden   {res}       -0.370*        (0.217)       -0.374*        (0.218)       -0.348*        (0.206){txt}
{txt}socprog     {res}        0.295         (0.232)        0.319         (0.229)        0.272         (0.210){txt}
{txt}male        {res}                                    -0.207         (0.250)       -0.166         (0.248){txt}
{txt}age         {res}                                     0.010         (0.009)        0.006         (0.008){txt}
{txt}edulevel    {res}                                    -0.023         (0.099)        0.013         (0.092){txt}
{txt}inclevel    {res}                                     0.042         (0.067)        0.085         (0.069){txt}
{txt}urb         {res}                                    -0.305         (0.204)       -0.382*        (0.202){txt}
{txt}ftemp       {res}                                                                 -0.231         (0.196){txt}
{txt}psj         {res}                                                                  0.119         (0.326){txt}
{txt}{hline 99}
{txt}N           {res}         1514                         1501                         1501                {txt}
{txt}r2          {res}                                                                                       {txt}
{txt}{hline 99}

{com}. 
.   
. 
. 
. 
. * Robustness test with ordered logit 

. 
. 
. 
.   ** ssc install gologit2

. 
.   qui gologit2 q3 hurt adjburden socprog govblame industryblame ngoblame [pw = Weight] if q9==1

. 
.   est store c31

. 
.   qui gologit2 q3 hurt adjburden socprog govblame industryblame ngoblame $X1 [pw = Weight] if q9==1

. 
.   est store c32

. 
.   qui xi:gologit2 q3 hurt adjburden socprog govblame industryblame ngoblame $X1 ftemp psj i.language [pw = Weight] if q9==1
{err}WARNING!  have an outcome with a predicted probability that is
less than 0. See the {stata whelp gologit2:gologit2 help} section on Warning Messages for more information.

{com}. 
.   est store c33

. 
.  
. 
.   ** Table A4 

. 
.   estout c3*, drop(_* *blame) starlevels(* .1 ** .05 *** .01) cells((b(star fmt(3)) se(par fmt(3)))) stats(N r2, fmt(0 3))  
{res}
{txt}{hline 99}
{txt}                      c31                          c32                          c33                
{txt}                        b              se            b              se            b              se
{txt}{hline 99}
{res}Disapprove~t                                                                                       {txt}
{txt}hurt        {res}        0.049         (0.197)        0.014         (0.193)        0.059         (0.180){txt}
{txt}adjburden   {res}       -0.194         (0.197)       -0.205         (0.190)       -0.095         (0.175){txt}
{txt}socprog     {res}        0.302         (0.202)        0.391**       (0.193)        0.356**       (0.181){txt}
{txt}male        {res}                                     0.052         (0.216)        0.126         (0.215){txt}
{txt}age         {res}                                     0.004         (0.007)       -0.003         (0.007){txt}
{txt}edulevel    {res}                                    -0.070         (0.086)       -0.060         (0.081){txt}
{txt}inclevel    {res}                                     0.082         (0.068)        0.141**       (0.067){txt}
{txt}urb         {res}                                    -0.295         (0.185)       -0.393**       (0.179){txt}
{txt}ftemp       {res}                                                                 -0.139         (0.170){txt}
{txt}psj         {res}                                                                  0.297         (0.290){txt}
{txt}{hline 99}
{res}Disapprove~t                                                                                       {txt}
{txt}hurt        {res}        0.048         (0.215)        0.073         (0.210)        0.115         (0.195){txt}
{txt}adjburden   {res}       -0.399*        (0.214)       -0.377*        (0.205)       -0.308         (0.189){txt}
{txt}socprog     {res}        0.326         (0.227)        0.307         (0.221)        0.212         (0.199){txt}
{txt}male        {res}                                    -0.145         (0.241)       -0.170         (0.238){txt}
{txt}age         {res}                                     0.008         (0.008)        0.002         (0.007){txt}
{txt}edulevel    {res}                                    -0.010         (0.095)        0.037         (0.089){txt}
{txt}inclevel    {res}                                     0.039         (0.065)        0.077         (0.069){txt}
{txt}urb         {res}                                    -0.331         (0.208)       -0.488**       (0.214){txt}
{txt}ftemp       {res}                                                                 -0.198         (0.191){txt}
{txt}psj         {res}                                                                  0.239         (0.317){txt}
{txt}{hline 99}
{res}Approve_so~t                                                                                       {txt}
{txt}hurt        {res}       -0.048         (0.304)        0.015         (0.309)        0.047         (0.288){txt}
{txt}adjburden   {res}        0.281         (0.290)        0.278         (0.331)        0.288         (0.347){txt}
{txt}socprog     {res}        0.066         (0.305)        0.028         (0.340)        0.005         (0.332){txt}
{txt}male        {res}                                    -0.186         (0.463)       -0.232         (0.449){txt}
{txt}age         {res}                                    -0.002         (0.010)       -0.008         (0.011){txt}
{txt}edulevel    {res}                                    -0.035         (0.133)       -0.039         (0.138){txt}
{txt}inclevel    {res}                                    -0.010         (0.079)       -0.002         (0.087){txt}
{txt}urb         {res}                                    -0.307         (0.303)       -0.484         (0.319){txt}
{txt}ftemp       {res}                                                                 -0.109         (0.237){txt}
{txt}psj         {res}                                                                 -0.229         (0.669){txt}
{txt}{hline 99}
{txt}N           {res}         1514                         1501                         1501                {txt}
{txt}r2          {res}                                                                                       {txt}
{txt}{hline 99}

{com}. 
.   
. 
.   
. 
. * Robustness test with all individuals

. 
. 
. 
.   qui reg prog_app i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame [pw = Weight]

. 
.   est store c41

. 
.   qui reg prog_app i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame $X1 $X2 [pw = Weight]

. 
.   est store c42

. 
. 
. 
.   qui reg confimf i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame [pw = Weight] 

. 
.   est store c43

. 
.   qui reg confimf i.hurt i.adjburden i.socprog i.govblame i.industryblame i.ngoblame $X1 $X2 [pw = Weight]

. 
.   est store c44

. 
. 
. 
.   ** Table A5

. 
.   estout c4*, drop(_* *0.* *blame) starlevels(* .1 ** .05 *** .01) cells((b(star fmt(3)) se(par fmt(3)))) stats(N r2, fmt(0 3)) 
{res}
{txt}{hline 128}
{txt}                      c41                          c42                          c43                          c44                
{txt}                        b              se            b              se            b              se            b              se
{txt}{hline 128}
{txt}1.hurt      {res}        0.007         (0.046)        0.019         (0.039)        0.167         (0.308)        0.257         (0.292){txt}
{txt}1.adjburden {res}       -0.079*        (0.046)       -0.070*        (0.040)       -0.198         (0.306)       -0.194         (0.291){txt}
{txt}1.socprog   {res}        0.037         (0.048)        0.029         (0.041)        0.176         (0.310)        0.129         (0.284){txt}
{txt}male        {res}                                    -0.037         (0.047)                                    -0.316         (0.323){txt}
{txt}age         {res}                                     0.001         (0.002)                                    -0.012         (0.012){txt}
{txt}edulevel    {res}                                     0.002         (0.018)                                     0.213*        (0.121){txt}
{txt}inclevel    {res}                                     0.026**       (0.012)                                    -0.124         (0.106){txt}
{txt}urb         {res}                                    -0.096**       (0.039)                                    -0.202         (0.282){txt}
{txt}ftemp       {res}                                    -0.047         (0.037)                                    -0.561**       (0.245){txt}
{txt}psj         {res}                                    -0.001         (0.062)                                     0.108         (0.411){txt}
{txt}1.language  {res}                                     0.000             (.)                                     0.000             (.){txt}
{txt}2.language  {res}                                    -0.112*        (0.059)                                    -0.489         (0.473){txt}
{txt}3.language  {res}                                     0.290***      (0.077)                                     0.690         (0.547){txt}
{txt}4.language  {res}                                     0.248**       (0.108)                                    -0.818         (0.904){txt}
{txt}5.language  {res}                                     0.098         (0.065)                                     0.255         (0.424){txt}
{txt}6.language  {res}                                     0.033         (0.053)                                    -0.466         (0.364){txt}
{txt}{hline 128}
{txt}N           {res}         1691                         1677                         1691                         1677                {txt}
{txt}r2          {res}        0.011                        0.081                        0.009                        0.045                {txt}
{txt}{hline 128}

{com}. 
. 
. 
. * Demographics that predict screening outcome 

. 
. 
. 
.   tab q9

{txt}Q9. The International Monetary Fund is? {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
An intergovernmental organisation that  {c |}{res}      1,514       89.53       89.53
{txt}An intergovernmental organisation that  {c |}{res}         43        2.54       92.08
{txt}An intergovernmental organisation that  {c |}{res}        134        7.92      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,691      100.00

{com}. 
.   
. 
.   g highed=edulevel>3

. 
.   g rich=inclevel>6

. 
.   g older=age>33

. 
.   
. 
.   mat M=J(7,4,0)

. 
.   
. 
.   ttest knowimf, by(highed) une

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}  1,283{col 22} .8838659{col 34} .0089481{col 46} .3205105{col 58} .8663115{col 70} .9014204
       {txt}1 {c |}{res}{col 12}    408{col 22} .9313725{col 34} .0125318{col 46} .2531299{col 58} .9067374{col 70} .9560077
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  1,691{col 22} .8953282{col 34} .0074467{col 46} .3062206{col 58} .8807225{col 70} .9099339
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0475066{col 34} .0153985{col 58}-.0777298{col 70}-.0172834
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -3.0851
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 857.073

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0010         {txt}Pr(|T| > |t|) = {res}0.0021          {txt}Pr(T > t) = {res}0.9990

{com}. 
.     mat M[1,1]=`r(mu_2)'

. 
. mat M[1,2]=`r(mu_1)'

. 
. mat M[1,3]=`r(mu_2)'-`r(mu_1)'

. 
. mat M[1,4]=`r(p)'

. 
.   qui ttest knowimf, by(rich) une

. 
.     mat M[2,1]=`r(mu_2)'

. 
. mat M[2,2]=`r(mu_1)'

. 
. mat M[2,3]=`r(mu_2)'-`r(mu_1)'

. 
. mat M[2,4]=`r(p)'

. 
.   qui ttest knowimf, by(urb) une

. 
.     mat M[3,1]=`r(mu_2)'

. 
. mat M[3,2]=`r(mu_1)'

. 
. mat M[3,3]=`r(mu_2)'-`r(mu_1)'

. 
. mat M[3,4]=`r(p)'

. 
.   qui ttest knowimf, by(ftemp) une

. 
.     mat M[4,1]=`r(mu_2)'

. 
. mat M[4,2]=`r(mu_1)'

. 
. mat M[4,3]=`r(mu_2)'-`r(mu_1)'

. 
. mat M[4,4]=`r(p)'

. 
.   qui ttest knowimf, by(psj) une

. 
.     mat M[5,1]=`r(mu_2)'

. 
. mat M[5,2]=`r(mu_1)'

. 
. mat M[5,3]=`r(mu_2)'-`r(mu_1)'

. 
. mat M[5,4]=`r(p)'

. 
.   qui ttest knowimf, by(male) une

. 
.     mat M[6,1]=`r(mu_2)'

. 
. mat M[6,2]=`r(mu_1)'

. 
. mat M[6,3]=`r(mu_2)'-`r(mu_1)'

. 
. mat M[6,4]=`r(p)'

. 
.   qui ttest knowimf, by(older) une

. 
.     mat M[7,1]=`r(mu_2)'

. 
. mat M[7,2]=`r(mu_1)'

. 
. mat M[7,3]=`r(mu_2)'-`r(mu_1)'

. 
. mat M[7,4]=`r(p)'

. 
. 
. 
. mat li M
{res}
{txt}M[7,4]
            c1          c2          c3          c4
r1 {res}  .93137255   .88386594   .04750661   .00209985
{txt}r2 {res}  .90979381   .89102072   .01877309   .26789533
{txt}r3 {res}  .91005803   .87214612   .03791191   .01647376
{txt}r4 {res}  .91808511   .86684421    .0512409   .00082986
{txt}r5 {res}  .87619048   .89659521  -.02040473   .53990103
{txt}r6 {res}  .90337838   .83886256   .06451582   .01564296
{txt}r7 {res}  .93045564   .86114352   .06931211   2.813e-06
{reset}
{com}. 
. 
. 
.   
. 
. * Heterogeneous effects

. 
. 
. 
.   local t=1

. 
.   foreach x in highed rich urb older ftemp male {c -(}
{txt}  2{com}. 
.   
. 
.     g x=`x'
{txt}  3{com}. 
.     qui reg prog_app i.hurt##i.x i.adjburden##i.x i.socprog##i.x i.govblame##i.x i.industryblame##i.x i.ngoblame##i.x [pw = Weight] if q9==1
{txt}  4{com}. 
. qui margins x, dydx(hurt adjburden socprog) post
{txt}  5{com}. 
. est store c5`t'
{txt}  6{com}. 
.     
. 
. qui reg confimf i.hurt##i.x i.adjburden##i.x i.socprog##i.x i.govblame##i.x i.industryblame##i.x i.ngoblame##i.x [pw = Weight] if q9==1
{txt}  7{com}. 
. qui margins x, dydx(hurt adjburden socprog) post
{txt}  8{com}. 
. est store c6`t'
{txt}  9{com}. 
.     
. 
. local t=`t'+1
{txt} 10{com}. 
. di "`x':"
{txt} 11{com}. 
. drop x
{txt} 12{com}. 
. {c )-}
highed:
rich:
urb:
older:
ftemp:
male:

. 
. 
. 
.   ** Table A6

. 
.   estout c5*, drop(*0b.*:) starlevels(* .1 ** .05 *** .01) cells((b(star fmt(3)) se(par fmt(3)))) stats(N r2, fmt(0 3))  
{res}
{txt}{hline 186}
{txt}                      c51                          c52                          c53                          c54                          c55                          c56                
{txt}                        b              se            b              se            b              se            b              se            b              se            b              se
{txt}{hline 186}
{res}1.hurt                                                                                                                                                                                    {txt}
{txt}0.x         {res}        0.012         (0.056)        0.008         (0.053)       -0.011         (0.068)       -0.058         (0.054)       -0.012         (0.071)        0.009         (0.093){txt}
{txt}1.x         {res}       -0.054         (0.087)       -0.022         (0.084)        0.022         (0.044)        0.057         (0.071)        0.029         (0.039)       -0.003         (0.029){txt}
{txt}{hline 186}
{res}1.adjburden                                                                                                                                                                               {txt}
{txt}0.x         {res}       -0.079         (0.055)       -0.090*        (0.052)       -0.074         (0.067)       -0.086         (0.053)       -0.116*        (0.067)       -0.166*        (0.089){txt}
{txt}1.x         {res}       -0.086         (0.081)       -0.030         (0.089)       -0.081*        (0.044)       -0.055         (0.067)       -0.026         (0.039)       -0.021         (0.029){txt}
{txt}{hline 186}
{res}1.socprog                                                                                                                                                                                 {txt}
{txt}0.x         {res}        0.068         (0.060)        0.079         (0.057)        0.095         (0.073)        0.113**       (0.053)        0.079         (0.073)        0.093         (0.099){txt}
{txt}1.x         {res}        0.016         (0.076)        0.008         (0.090)        0.020         (0.045)        0.019         (0.074)        0.025         (0.040)        0.021         (0.029){txt}
{txt}{hline 186}
{txt}N           {res}         1514                         1514                         1514                         1514                         1514                         1514                {txt}
{txt}r2          {res}                                                                                                                                                                              {txt}
{txt}{hline 186}

{com}. 
. 
. 
.   ** Table A7

. 
.   estout c6*, drop(*0b.*:) starlevels(* .1 ** .05 *** .01) cells((b(star fmt(3)) se(par fmt(3)))) stats(N r2, fmt(0 3))  
{res}
{txt}{hline 186}
{txt}                      c61                          c62                          c63                          c64                          c65                          c66                
{txt}                        b              se            b              se            b              se            b              se            b              se            b              se
{txt}{hline 186}
{res}1.hurt                                                                                                                                                                                    {txt}
{txt}0.x         {res}        0.082         (0.392)        0.144         (0.378)        0.208         (0.487)        0.871**       (0.407)        0.075         (0.486)        0.208         (0.642){txt}
{txt}1.x         {res}        0.396         (0.484)        0.260         (0.504)        0.187         (0.307)       -0.528         (0.442)        0.383         (0.251)        0.165         (0.194){txt}
{txt}{hline 186}
{res}1.adjburden                                                                                                                                                                               {txt}
{txt}0.x         {res}       -0.281         (0.385)       -0.134         (0.373)       -0.321         (0.479)        0.255         (0.411)       -0.622         (0.485)       -0.494         (0.661){txt}
{txt}1.x         {res}       -0.278         (0.501)       -0.699         (0.518)       -0.188         (0.293)       -0.594         (0.407)        0.032         (0.252)       -0.146         (0.195){txt}
{txt}{hline 186}
{res}1.socprog                                                                                                                                                                                 {txt}
{txt}0.x         {res}        0.264         (0.401)        0.248         (0.387)        0.413         (0.496)        0.362         (0.434)        0.107         (0.503)        0.427         (0.675){txt}
{txt}1.x         {res}       -0.014         (0.507)       -0.084         (0.505)       -0.065         (0.306)       -0.072         (0.439)        0.453*        (0.254)        0.169         (0.197){txt}
{txt}{hline 186}
{txt}N           {res}         1514                         1514                         1514                         1514                         1514                         1514                {txt}
{txt}r2          {res}                                                                                                                                                                              {txt}
{txt}{hline 186}

{com}. 
. 
. 
.   
. 
. * Combined messages

. 
. 
. 
.   g onlypos=(socprog==1) & (hurt==0 & adjburden==0)

. 
.   g onlyneg=((hurt==1|adjburden==1) & socprog==0)

. 
.   g mixed=((hurt==1|adjburden==1) & socprog==1)

. 
.   
. 
.   g MessageType=1*(onlypos==1)+2*(onlyneg==1)+3*(mixed==1)

. 
.   tab MessageType

{txt}MessageType {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        211       12.48       12.48
{txt}          1 {c |}{res}        209       12.36       24.84
{txt}          2 {c |}{res}        645       38.14       62.98
{txt}          3 {c |}{res}        626       37.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,691      100.00

{com}. 
.  
. 
.   ** Figure A5 

. 
.   graph bar prog_app [pw=Weight] if q9==1, over(MessageType) scheme(s1mono) ytitle(Percent)
{res}
{com}. 
.   
. 
.   
. 
. 
. 
. * Descriptive statistics 

. 
.   
. 
.   qui estpost su prog_app conf hurt adjb socp $X1 fte psj lang Wei q9

. 
.   esttab ., cells("count(fmt(0)) mean(fmt(3)) sd(fmt(3)) min(fmt(3)) max(fmt(3))")
{res}
{txt}{hline 77}
{txt}                      (1)                                                    
{txt}                                                                             
{txt}                    count         mean           sd          min          max
{txt}{hline 77}
{txt}prog_app    {res}         1691        0.305        0.461        0.000        1.000{txt}
{txt}confimf     {res}         1691        2.918        3.229        0.000       10.000{txt}
{txt}hurt        {res}         1691        0.497        0.500        0.000        1.000{txt}
{txt}adjburden   {res}         1691        0.512        0.500        0.000        1.000{txt}
{txt}socprog     {res}         1691        0.494        0.500        0.000        1.000{txt}
{txt}male        {res}         1691        0.875        0.331        0.000        1.000{txt}
{txt}age         {res}         1691       35.664       12.870       18.000       81.000{txt}
{txt}edulevel    {res}         1677        2.741        1.192        1.000        5.000{txt}
{txt}inclevel    {res}         1691        5.517        1.539        1.000        8.000{txt}
{txt}urb         {res}         1691        0.611        0.488        0.000        1.000{txt}
{txt}ftemp       {res}         1691        0.556        0.497        0.000        1.000{txt}
{txt}psj         {res}         1691        0.062        0.241        0.000        1.000{txt}
{txt}language    {res}         1691        2.613        1.959        1.000        6.000{txt}
{txt}Weight      {res}         1691        1.000        1.793        0.159       33.720{txt}
{txt}q9          {res}         1691        1.184        0.556        1.000        3.000{txt}
{txt}{hline 77}
{txt}N           {res}         1691                                                    {txt}
{txt}{hline 77}

{com}. 
. 
. 
.   
. 
. * Additional plots - outcome distribution 

. 
. 
. 
.   ** Figure A1

. 
.   graph bar if q9==1, over(q3, label(angle(90))) scheme(s1mono) ytitle(Percent)
{res}
{com}. 
.   
. 
.   ** Figure A2

. 
.   graph bar if q9==1, over(q6, relabel(1 "0" 11 "10") label(angle(0))) scheme(s1mono) ytitle(Percent)
{res}
{com}. 
.   
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\BERNI\Forschung\GOAT-ARS\Submission\FPA\FPA replication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Dec 2025, 08:43:36
{txt}{.-}
{smcl}
{txt}{sf}{ul off}